This introduces methods for extracting and analyzing social network data from Twitter for hashtag conversations (and emergent events), event graphs, search networks, and user ego neighborhoods (using NodeXL). There will be direct demonstrations and discussions of how to analyze social network graphs. This information may be extended with human- and / or machine-based sentiment analysis.
Understanding Public Sentiment: Conducting a Related-Tags Content Network Ext...Shalin Hai-Jew
This presentation focuses on how to understand public sentiment through a related-tags content network analysis of public Flickr photos and videos. NodeXL is used to conduct data extractions and visualizations of user-tagged Flickr contents and the resulting “noisy” folksonomies. What mental connections may be made about particular issues based on analysis of text-annotated graphs?
Exploring Article Networks on Wikipedia with NodeXLShalin Hai-Jew
With 4.7 million articles in the English version of Wikipedia, this crowd-sourced online encyclopedia is regularly one of the top-ten visited sites online. For many, this is the go-to source for a first read on a topic. The open-source and free Network Overview, Discovery and Exploration for Excel (NodeXL), which is an add-on to Microsoft Excel, enables the capture of “article networks” from Wikipedia. Such content network analysis-based data visualizations enable the development of research leads; some understandings of public conceptualizations of related concepts, peoples, events, and phenomena; the profiling of Wikipedia editors (both humans and ‘bots), and other research insights. This presentation will showcase this affordance of NodeXL and provide some ideas for practical applications of this channel of research and knowing.
Eavesdropping on the Twitter Microblogging SiteShalin Hai-Jew
Research analysts go to Twitter to capture the general trends of public conversations, identify and profile influential accounts, and extract subgroups within larger collectives and larger discourses; they also go to eavesdrop on individual self-talk and individual-to-individual conversations. So what is technically in your tweets, asked Dave Rosenberg famously in a CNET article (2010). The answer: a whole lot more than 140 characters. How are the most influential social media accounts identified through #hashtag graphs? How are themes extracted? How are sentiments understood? How can users be profiled through their Tweetstreams? How can locations be mapped in terms of the Twitter conversations occurring in particular physical areas? How can live and trending issues be identified and categorized in terms of sentiment (positive, negative, and neutral)? This presentation will summarize some of the free and open-source tools as well as commercial and proprietary ones that enable increased knowability.
Coding Social Imagery: Learning from a #selfie #humor Image Set from InstagramShalin Hai-Jew
Social media messaging has long been harnessed to inform faculty about their respective learners. The textual channel is often used because of the ease of interpretation and analysis. Social imagery—tagged images, #selfies, grouped imagery, and others—has been less used, in part because images are more complex and multi-meaninged to analyze. Also, there are not many generalist models that inform how to code or even understand social imagery in an emergent way. (There are large-scale computational means to interpret online images, such as the AlchemyAPI of IBM Watson, for various types of feature extractions. There are ways to code imagery based on specific research questions in particular fields-of-practice.)
The presenter recently analyzed a 941-image #selfie + #humor image set from Instagram, with three main research questions:
What does identity-based humor look like in terms of a #selfie #humor- tagged image set from the Instagram photo-sharing mobile app?
Do more modern forms of mediated social humor link to more traditional forms theoretically? Is it possible to apply the Humor Styles Model to the images from the #selfie #humor Instagram image set to better understand #selfie #humor?
What are some constructive and systematized ways to analyze social image sets manually (with some computational support)?
This digital poster session will highlight some of the initial research findings (forthcoming in a near-future publication) and share insights about effectively coding social imagery in a bottom-up and emergent way.
Using Maltego Tungsten to Explore Cyber-Physical Confluence in GeolocationShalin Hai-Jew
This presentation highlights a software tool that can run "machines" and "transforms" on the public Web to extract information powerfully. In this instance, this highlights how online information may be turned to geolocation data.
30 Tools and Tips to Speed Up Your Digital Workflow Mike Kujawski
Have you ever found yourself wasting a considerable amount of time performing some annoying, repetitive process within a common application, social media website, or your web browser? Wish there was a "magic" shortcut or simply a better way of getting it done? There most likely is.
While having a solid strategy should always be the first priority before engaging in the digital/social media space, it's also smart to arm yourself with a set of tools that will help you with the tactical implementation of your plan. These presentation slides provide 30 tools and tips hand-picked from Mike Kujawski's personal experience, day-to-day observations, and interactions with his consulting and training clients.
These tools are meant to help you be more efficient and effective as a communicator in today's digital world, where agility and "life-hacking" skills are becoming increasingly valued.
Social Data and Multimedia Analytics for News and Events ApplicationsYiannis Kompatsiaris
The keynote discusses a framework enabling real-time multimedia indexing and search across multiple social media sources. It places particular emphasis on the real-time, social and contextual nature of content and information consumption in order to integrate topic and event detection, mining, search and retrieval, based on aggregation and indexing of shared user-generated multimedia content. User-friendly applications for the News and Events domains have been developed based on these approaches, incorporating novel user-centric media visualisation and browsing methods. The research and development is part of the FP7 EU project SocialSensor.
Content:
Introduction
Motivation – Challenges
SocialSensor Project and Use Cases
Research Approaches
Large-Scale visual search
Clustering
Verification
Demos – Applications
MM News Demo
Clusttour
Thessfest
Conclusions
Understanding Public Sentiment: Conducting a Related-Tags Content Network Ext...Shalin Hai-Jew
This presentation focuses on how to understand public sentiment through a related-tags content network analysis of public Flickr photos and videos. NodeXL is used to conduct data extractions and visualizations of user-tagged Flickr contents and the resulting “noisy” folksonomies. What mental connections may be made about particular issues based on analysis of text-annotated graphs?
Exploring Article Networks on Wikipedia with NodeXLShalin Hai-Jew
With 4.7 million articles in the English version of Wikipedia, this crowd-sourced online encyclopedia is regularly one of the top-ten visited sites online. For many, this is the go-to source for a first read on a topic. The open-source and free Network Overview, Discovery and Exploration for Excel (NodeXL), which is an add-on to Microsoft Excel, enables the capture of “article networks” from Wikipedia. Such content network analysis-based data visualizations enable the development of research leads; some understandings of public conceptualizations of related concepts, peoples, events, and phenomena; the profiling of Wikipedia editors (both humans and ‘bots), and other research insights. This presentation will showcase this affordance of NodeXL and provide some ideas for practical applications of this channel of research and knowing.
Eavesdropping on the Twitter Microblogging SiteShalin Hai-Jew
Research analysts go to Twitter to capture the general trends of public conversations, identify and profile influential accounts, and extract subgroups within larger collectives and larger discourses; they also go to eavesdrop on individual self-talk and individual-to-individual conversations. So what is technically in your tweets, asked Dave Rosenberg famously in a CNET article (2010). The answer: a whole lot more than 140 characters. How are the most influential social media accounts identified through #hashtag graphs? How are themes extracted? How are sentiments understood? How can users be profiled through their Tweetstreams? How can locations be mapped in terms of the Twitter conversations occurring in particular physical areas? How can live and trending issues be identified and categorized in terms of sentiment (positive, negative, and neutral)? This presentation will summarize some of the free and open-source tools as well as commercial and proprietary ones that enable increased knowability.
Coding Social Imagery: Learning from a #selfie #humor Image Set from InstagramShalin Hai-Jew
Social media messaging has long been harnessed to inform faculty about their respective learners. The textual channel is often used because of the ease of interpretation and analysis. Social imagery—tagged images, #selfies, grouped imagery, and others—has been less used, in part because images are more complex and multi-meaninged to analyze. Also, there are not many generalist models that inform how to code or even understand social imagery in an emergent way. (There are large-scale computational means to interpret online images, such as the AlchemyAPI of IBM Watson, for various types of feature extractions. There are ways to code imagery based on specific research questions in particular fields-of-practice.)
The presenter recently analyzed a 941-image #selfie + #humor image set from Instagram, with three main research questions:
What does identity-based humor look like in terms of a #selfie #humor- tagged image set from the Instagram photo-sharing mobile app?
Do more modern forms of mediated social humor link to more traditional forms theoretically? Is it possible to apply the Humor Styles Model to the images from the #selfie #humor Instagram image set to better understand #selfie #humor?
What are some constructive and systematized ways to analyze social image sets manually (with some computational support)?
This digital poster session will highlight some of the initial research findings (forthcoming in a near-future publication) and share insights about effectively coding social imagery in a bottom-up and emergent way.
Using Maltego Tungsten to Explore Cyber-Physical Confluence in GeolocationShalin Hai-Jew
This presentation highlights a software tool that can run "machines" and "transforms" on the public Web to extract information powerfully. In this instance, this highlights how online information may be turned to geolocation data.
30 Tools and Tips to Speed Up Your Digital Workflow Mike Kujawski
Have you ever found yourself wasting a considerable amount of time performing some annoying, repetitive process within a common application, social media website, or your web browser? Wish there was a "magic" shortcut or simply a better way of getting it done? There most likely is.
While having a solid strategy should always be the first priority before engaging in the digital/social media space, it's also smart to arm yourself with a set of tools that will help you with the tactical implementation of your plan. These presentation slides provide 30 tools and tips hand-picked from Mike Kujawski's personal experience, day-to-day observations, and interactions with his consulting and training clients.
These tools are meant to help you be more efficient and effective as a communicator in today's digital world, where agility and "life-hacking" skills are becoming increasingly valued.
Social Data and Multimedia Analytics for News and Events ApplicationsYiannis Kompatsiaris
The keynote discusses a framework enabling real-time multimedia indexing and search across multiple social media sources. It places particular emphasis on the real-time, social and contextual nature of content and information consumption in order to integrate topic and event detection, mining, search and retrieval, based on aggregation and indexing of shared user-generated multimedia content. User-friendly applications for the News and Events domains have been developed based on these approaches, incorporating novel user-centric media visualisation and browsing methods. The research and development is part of the FP7 EU project SocialSensor.
Content:
Introduction
Motivation – Challenges
SocialSensor Project and Use Cases
Research Approaches
Large-Scale visual search
Clustering
Verification
Demos – Applications
MM News Demo
Clusttour
Thessfest
Conclusions
In this talk we shall introduce the main ideas of TruSIS (Trust in Social Internetworking System), a Marie Curie Fellowhsip financed by European Union and hosted at VU University, Department of Computer Science, Business and Web group. The goal of TruSIS is to study the baheviour of users who affiliate to multiple social networking sites and
are active in them (e.g., users may publish personal profiles on sites like MySpace and post videos on sites like YouTube). We briefly called this scenario as SIS (Social Internetworking system).
As a first research contribution, we implemented a crawler to gather data about users and link their profiles on multiple social networking websites. To this purpose we used Google Social Graph API, a powerful API released by Google in 2008. We obtained a sample of about 1.3 millions of user accounts and 36 millions of connections between them.
Parameters from social network theory (like average clustering coefficient, network modularity and so on) were used to study the structural properties of the gathered sample and how these properties depend on user behavious.
A second contribution is about the computation of distance between two users in a SIS on the basis of their social ties. We used a popular parameter from Social Network Theory known as Katz coeffcient and
provide a computationally afficient approach to computing Katz coefficient which relies on the usage of a popular tool from linear algebra known as Sherman- Morrison formula.
Finally, we shall describe our work on extending the notion of trust from single social networks to a SIS. We describe the main research challenges tied to the definition of trust and how they relate to Semantic Web technologies.
Picturing the Social: Talk for Transforming Digital Methods Winter SchoolFarida Vis
This talk highlights the work of the Visual Social Media Lab and the Picturing the Social project. It summarises the key research questions and aims of the project. It highlights the value of interdisciplinarity and working closely with industry in this area. It also focuses on the way in which me might study different types of structures involved in the circulation and the scopic regimes that make social media images more or less visible. It also tries to unpack how we can start to think about APIs as 'method' and looks at the different ways in which we can get access to different kinds of social media image data. Both through public ('free') APIs and ('pay for') firehose data.
A glimpse into what social media is all about and how the researchers in the world are using social media. Social media is not a mere hype and not a platform to leverage word-of-the-mouth practices as is the common perception of it in Pakistan: it is much more than that and this is what this talk presented.
Social Media Mining - Chapter 10 (Behavior Analytics)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Twitter analytics: some thoughts on sampling, tools, data, ethics and user re...Farida Vis
Keynote delivered at the SRA Social Media in Social Research conference, London, 24 June, 2013. The presentation highlights some thoughts on sampling, tools, data, ethics and user requirements for Twitter analytics, including an overview of a series of recent tools.
Mining and Comparing Engagement Dynamics Across Multiple Social Media Platfor...The Open University
Understanding what attracts users to engage with social media content is important in domains such as market analytics, advertising, and community management.
To date, many pieces of work have examined engagement dynamics in isolated platforms with little consideration or assessment of how these dynamics might vary between disparate social media systems. Additionally, such explorations have often used different features and notions of engagement, thus rendering the cross-platform comparison of engagement dynamics limited. In this paper we define a common framework of engagement analysis and examine and compare engagement dynamics across five social media platforms: Facebook, Twitter, Boards.ie, Stack Overflow and the SAP Community Network. We define a variety of common features (social and content) to capture the dynamics that correlate with engagement in multiple social media platforms, and present an evaluation pipeline intended to enable cross-platform comparison. Our comparison results demonstrate the varying factors at play in different platforms, while also exposing several similarities.
Social Media Mining - Chapter 9 (Recommendation in Social Media)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 6 (Community Analysis)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
LIWC-ing at Texts for Insights from Linguistic PatternsShalin Hai-Jew
Since the mid-1990s, researchers have been using the Linguistic Inquiry and Word Count (LIWC pronounced “luke”) software tool to explore various text corpora for hidden insights from linguistic patterns. The LIWC tool has evolved over the years. Simultaneously, research using computational text analysis has evolved and shed light on areas of deception, threat assessment, personality, predictive analytics, and other areas. This presentation will highlight some of the applications of LIWC in the research literature and showcase the tool on some original text sets.
Formations & Deformations of Social Network GraphsShalin Hai-Jew
Social network graphs are node-link (vertex-edge; entity-relationship) diagrams that show relationships between people and groups. Open-source tools like NodeXL Basic (available on Microsoft’s CodePlex) enable the capture of network data from select social media platforms through third-party add-ons and social media APIs. From social groups, relational clusters are extracted with clustering algorithms which identify intensities of connections. Visually, structural relational data is conveyed with layout algorithms in two-dimensional space. Using these various layout options and built-in visual design features, it is possible to aesthetically “deform” the network graph data for visual effects. This presentation introduces novel datasets and novel data visualizations.
In this talk we shall introduce the main ideas of TruSIS (Trust in Social Internetworking System), a Marie Curie Fellowhsip financed by European Union and hosted at VU University, Department of Computer Science, Business and Web group. The goal of TruSIS is to study the baheviour of users who affiliate to multiple social networking sites and
are active in them (e.g., users may publish personal profiles on sites like MySpace and post videos on sites like YouTube). We briefly called this scenario as SIS (Social Internetworking system).
As a first research contribution, we implemented a crawler to gather data about users and link their profiles on multiple social networking websites. To this purpose we used Google Social Graph API, a powerful API released by Google in 2008. We obtained a sample of about 1.3 millions of user accounts and 36 millions of connections between them.
Parameters from social network theory (like average clustering coefficient, network modularity and so on) were used to study the structural properties of the gathered sample and how these properties depend on user behavious.
A second contribution is about the computation of distance between two users in a SIS on the basis of their social ties. We used a popular parameter from Social Network Theory known as Katz coeffcient and
provide a computationally afficient approach to computing Katz coefficient which relies on the usage of a popular tool from linear algebra known as Sherman- Morrison formula.
Finally, we shall describe our work on extending the notion of trust from single social networks to a SIS. We describe the main research challenges tied to the definition of trust and how they relate to Semantic Web technologies.
Picturing the Social: Talk for Transforming Digital Methods Winter SchoolFarida Vis
This talk highlights the work of the Visual Social Media Lab and the Picturing the Social project. It summarises the key research questions and aims of the project. It highlights the value of interdisciplinarity and working closely with industry in this area. It also focuses on the way in which me might study different types of structures involved in the circulation and the scopic regimes that make social media images more or less visible. It also tries to unpack how we can start to think about APIs as 'method' and looks at the different ways in which we can get access to different kinds of social media image data. Both through public ('free') APIs and ('pay for') firehose data.
A glimpse into what social media is all about and how the researchers in the world are using social media. Social media is not a mere hype and not a platform to leverage word-of-the-mouth practices as is the common perception of it in Pakistan: it is much more than that and this is what this talk presented.
Social Media Mining - Chapter 10 (Behavior Analytics)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Twitter analytics: some thoughts on sampling, tools, data, ethics and user re...Farida Vis
Keynote delivered at the SRA Social Media in Social Research conference, London, 24 June, 2013. The presentation highlights some thoughts on sampling, tools, data, ethics and user requirements for Twitter analytics, including an overview of a series of recent tools.
Mining and Comparing Engagement Dynamics Across Multiple Social Media Platfor...The Open University
Understanding what attracts users to engage with social media content is important in domains such as market analytics, advertising, and community management.
To date, many pieces of work have examined engagement dynamics in isolated platforms with little consideration or assessment of how these dynamics might vary between disparate social media systems. Additionally, such explorations have often used different features and notions of engagement, thus rendering the cross-platform comparison of engagement dynamics limited. In this paper we define a common framework of engagement analysis and examine and compare engagement dynamics across five social media platforms: Facebook, Twitter, Boards.ie, Stack Overflow and the SAP Community Network. We define a variety of common features (social and content) to capture the dynamics that correlate with engagement in multiple social media platforms, and present an evaluation pipeline intended to enable cross-platform comparison. Our comparison results demonstrate the varying factors at play in different platforms, while also exposing several similarities.
Social Media Mining - Chapter 9 (Recommendation in Social Media)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Media Mining - Chapter 6 (Community Analysis)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
LIWC-ing at Texts for Insights from Linguistic PatternsShalin Hai-Jew
Since the mid-1990s, researchers have been using the Linguistic Inquiry and Word Count (LIWC pronounced “luke”) software tool to explore various text corpora for hidden insights from linguistic patterns. The LIWC tool has evolved over the years. Simultaneously, research using computational text analysis has evolved and shed light on areas of deception, threat assessment, personality, predictive analytics, and other areas. This presentation will highlight some of the applications of LIWC in the research literature and showcase the tool on some original text sets.
Formations & Deformations of Social Network GraphsShalin Hai-Jew
Social network graphs are node-link (vertex-edge; entity-relationship) diagrams that show relationships between people and groups. Open-source tools like NodeXL Basic (available on Microsoft’s CodePlex) enable the capture of network data from select social media platforms through third-party add-ons and social media APIs. From social groups, relational clusters are extracted with clustering algorithms which identify intensities of connections. Visually, structural relational data is conveyed with layout algorithms in two-dimensional space. Using these various layout options and built-in visual design features, it is possible to aesthetically “deform” the network graph data for visual effects. This presentation introduces novel datasets and novel data visualizations.
This slideshow highlights the Tweet Analyzer machine, a tool created by Paterva and enabled through Maltego Carbon 3.5.3 and Maltego Chlorine 3.6.0. The Tweet Analyzer enables real-time captures of Tweets (from Twitter's streaming API) along with real-time sentiment analysis (based on polarities: positive, negative, and neutral), based on the Alchemy API.
Using Qualtrics to Create Automated Online TrainingsShalin Hai-Jew
When thinking about “transformational teaching and learning,” training would not be the first thing to come to mind.
The Qualtrics® research suite offers a number of design tools and features that enable the building of automated online trainings. There are the baseline features such as the ability to integrate multimedia, apply various question designs, enable accessibility features (like alt-texting), deliver a mobile experience, reach learners across distances, and provide basic security and data integrity features.
Other features actually make this tool phenomenally powerful. One is the ability to richly customize learning sequences—by learner profile, by performance (behavior), by selection, or a mix of factors. There is a feature that enables the scoring of learner responses and the ability to set a threshold for passing. This tool has a rich data analytics capability (including a light item analysis), including online analytics and even cross-tabulation analysis. A Qualtrics® API enables the recording of online assessment scores and learner behaviors, in an automated way to faculty / staff / student information systems.
Trainings are critical for effective workplace functioning and professional development. The same features in Qualtrics® that enable the effective building of automated trainings also enable the effective building of pre-learning modules or sequences for learners who need to refresh their skills for a new course. This digital slideshow introduces the use of Qualtrics® as a customizable training and pre-learning module tool.
Writing and Publishing about Applied Technologies in Tech Journals and BooksShalin Hai-Jew
This slideshow provides insights on how to write and publish about applied technologies in tech journals and books, including the following:
Getting started in tech publishing
Cost-benefit calculations
Parts to an article; parts to a chapter
Writing process
Collaborating
Publishing process
Acquiring readers (and citations)
Post-publishing
Next works
Researchers have long known that the words of a text have always contained more information than on the surface. As such, texts have been studied for subtexts and other latent or hidden information. One approach has involved the machine-enabled analysis of human sentiment, usually mapped out on a positive-negative polarity. NVivo 11 Plus (a qualitative research tool released in late 2015) enables the automated sentiment analysis of texts (coded research, formal articles, text corpora, Tweetstream datasets, Facebook wall posts, websites, and other sources) based on four categories: very positive, moderately positive, moderately negative, and very negative. The tool feature compares the target text set against a sentiment dictionary and enables coding at different units of analysis: sentence, paragraph, or cell. Further, the sentiment capability extracts the coded text into respective text sets which may be further analyzed using text frequency counts, text searches, automated theme and sub-theme extractions (topic modeling), and data visualizations.
Building a Digital Learning Object w/ Articulate Storyline 2Shalin Hai-Jew
The digital learning object (DLO) is still a common staple in online learning. One of the more sophisticated authoring tools to build DLOs is Articulate Storyline 2, which enables the integration of multimedia (including screen captures with Articulate Replay); the building of animations; branching, and other features. Its packaging allows a full range of SCORM and Tin Can API outputs and versioning in HTML 5. This presentation will introduce the software tool and some of its capabilities to provide a sense of where digital learning objects may be headed.
Fully Exploiting Qualitative and Mixed Methods Data from Online SurveysShalin Hai-Jew
A wide range of contemporary research uses online surveys. This presentation provides an overview of ways to exploit survey-captured data for analysis. There will be a summary of basic survey and item analysis that may be achieved with survey data results. There will also be a range of tips for extracting, cleaning, structuring, and presenting both quantitative and qualitative data for data-consumer sense-making. The platform that will be used as an exemplar will be the Qualtrics survey platform, and two supporting tools used for analysis are Excel 2013 and NVivo 10. Real-world projects are used to demo these approaches—with principal investigator (PI) permission.
Designing Online Learning to Actual Human CapabilitiesShalin Hai-Jew
In instructional design work, instructional designers (IDs) often focus on the changing technological capabilities (of authoring tools, of learning management systems, and so on)—namely, on enablements / affordances and constraints. What is less often discussed are human capabilities, their affordances and constraints. Human enablements may be broadly conceptualized as the following: (1) perception (five senses and proprioception), (2) cognition, (3) learning, (4) memory, (5) decision-making, and (6) action-taking. This presentation summarizes some of the latest research on these areas of human capabilities and some design mitigations to design for these particular aspects of people.
See Ya! Creating a Custom Spatial-Based Linguistic Analysis Dictionary from ...Shalin Hai-Jew
American Renunciation of Citizenship (by the numbers)
LIWC2015 and Custom Dictionaries
Tapping Twitter, Facebook, Flickr, Wikipedia, and Reddit
The “See Ya!” Dictionary
Lessons about Custom Spatial-Based Dictionary-Making
Space, Place, and the Renunciation of U.S. Citizenship (from social media datasets)
Some Future Research Directions
Capitalizing on Machine Reading to Engage Bigger DataShalin Hai-Jew
What are some ways to select, say, 200 research articles to “close read” from a set of 2,000 PDF articles gleaned from library databases and Google Scholar? How can a researcher make sense of a trending issue in the flood of Tweets and RT based on a particular hashtag (#) or keyword search or an especially lively Tweetstream based on a particular social media account? People are dealing with ever more prodigious amounts of information—from a number of sources. Those who are savvy to the uses of computers to aid their reading (through “distant reading” or “not-reading”) may find that they are able to cover much more ground. This presentation introduces the use of NVivo 11 Plus (matrix queries, word frequency counts, text searches and dendrograms, cluster analyses, topic modeling, and others) for multiple cases of distant reading to aid in academic and research work.
What is NodeXL (Network Overview, Discovery and Exploration for Excel)?
Graph aesthetics in NodeXL
Visual pleasure
Cognitive pleasure
Bridging to NodeXL for research and analysis
"Mass Surveillance" through Distant ReadingShalin Hai-Jew
Distant reading refers to the uses of computers to “read” texts by counting words, identifying themes and subthemes (through topic modeling), extracting sentiment, applying psychological analysis to the author(s), and otherwise finding latent or hidden insights. This work is based on research on “mass surveillance” based on five text sets: academic, mainstream journalism, microblogging, Wikipedia articles, and leaked government data. The purpose was to capture some insights about the collective social discussions occurring around this issue in an indirect way. This presentation uses a variety of data visualizations (article network graphs, word trees, dendrograms, treemaps, cluster diagrams, line graphs, bar charts, pie charts, and others) to show how machines read and the types of summary data they enable (at computational speeds, at machine scale, and in a reproducible way). Also, some computational linguistic analysis tools enable the creation of custom dictionaries for unique types of applied research. The tools used in this presentation include NVivo 11 Plus and LIWC2015.
This presentation explains the research I made during while working at the Social Computing Lab at KAIST.
The main goal was to expand the LIWC vocabulary and adapt for Twiter sentiment analysis.
Download it to see the animations :)
Letting the Machine Code Qualitative and Mixed Methods Data in NVivo 10Shalin Hai-Jew
An experimental feature in NVivo 10 (circa 2013), Autocoding by Existing Pattern, enables the application of semi-supervised machine learning to ingested research data. This results in the extraction of themes and other relevant insights from data—at machine speeds, based on the classification algorithm. This presentation will introduce this feature in NVivo 10 (on both Windows and Mac platforms). This will show how the machine can achieve high inter-rater reliability (a Cohen’s Kappa of one in many cases) on the one hand but still not achieve full human sensibility from “close reading” coding on the other. This presentation will suggest a complementary balance between machine- and human- coding of qualitative and mixed methods data for the most efficient application of researcher time and expertise.
Native Emigration from the U.S. and Renunciation of U.S. Citizenship Shalin Hai-Jew
This presentation summarizes some initial research on the phenomena of the renunciation of U.S. citizenship and green card status. This presentation highlights some of the basic literature and then uses some social media to tap an indirect sense of public attitudes towards this and peripherally related issues.
This slideshow reviews some of the features and functionalities of Qualtrics that enable its use in online trainings. This explores some important instructional design elements in online trainings, including for three main types: policy compliance, mass-scale trainings, and customized trainings. This reviews some core elements of online trainings. Finally, there are some reflections on real-world considerations when building an online training on Qualtrics.
Citizen Sensor Data Mining, Social Media Analytics and ApplicationsAmit Sheth
Opening talk at Singapore Symposium on Sentiment Analysis (S3A), February 6, 2015, Singapore. http://s3a.sentic.net/#s3a2015
Abstract
With the rapid rise in the popularity of social media, and near ubiquitous mobile access, the sharing of observations and opinions has become common-place. This has given us an unprecedented access to the pulse of a populace and the ability to perform analytics on social data to support a variety of socially intelligent applications -- be it for brand tracking and management, crisis coordination, organizing revolutions or promoting social development in underdeveloped and developing countries.
I will review: 1) understanding and analysis of informal text, esp. microblogs (e.g., issues of cultural entity extraction and role of semantic/background knowledge enhanced techniques), and 2) how we built Twitris, a comprehensive social media analytics (social intelligence) platform.
I will describe the analysis capabilities along three dimensions: spatio-temporal-thematic, people-content-network, and sentiment-emption-intent. I will couple technical insights with identification of computational techniques and real-world examples using live demos of Twitris (http://twitris2.knoesis.org).
Univ. of AZ Global Racing Symposium 2015 - Digital Strategiessmfrisby
Provides a high-level view of how organizations can leverage Big Data in the digital space. Covers topics such as structured vs unstructured data, curating disparate data sources and exploiting the data correlation opportunities.
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
Researching Social Media – Big Data and Social Media AnalysisFarida Vis
Researching Social Media – Big Data and Social Media Analysis, presentation for the Social Media for Researchers: A Sheffield Universities Social Media Symposium, 23 September 2014
One Web of pages, One Web of peoples, One Web of Services, One Web of Data, O...Fabien Gandon
Keynote Fabien GANDON, at WIM2016: One Web of pages, One Web of peoples, One Web of Services, One Web of Data, One Web of Things…and with the Semantic Web bind them.
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Keynote by Seth Grimes, presented at the Knowledge Extraction from Social Media workshop, November 12, 2012, preceding the International Semantic Web Conference
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
Review of trends related to social network analysis in the enterprise. Presented at the 2010 Catalyst Conference in San Diego, CA july 29, 2010. Presented with Mike Gotta, Gartner Group.
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Introduction to the Responsible Use of Social Media Monitoring and SOCMINT ToolsMike Kujawski
These are my slides from a custom tool-based demonstration workshop I was asked to do where I went over various free tools that can be used to obtain valuable public data.
For my final year project I used data analysis techniques to investigate user behavior pattern recognition in respect of similar interests and culture versus offline geographical location. This was an out-of-the-box topic, which I selected due to my love on Data Analysis, in respect of the Social Network Analysis in the Internet era.
How can we mine, analyse and visualise the Social Web?
In this lecture, you will learn about mining social web data for analysis. Data preparation and gathering basic statistics on your data.
Long nonfiction chapters are not in-style and may never have been. Where average chapter lengths of nonfiction book chapters are about 4,000 – 7,000 words in length, some may be several times that max range number. The explanation is that there is some irreducible complexity that that chapter addresses that cannot be addressed in shorter form. This slideshow explores some methods for writing longer chapters while still maintaining coherence, focus, and reader interest…and while using some technological tools to write and edit more efficiently.
Overcoming Reluctance to Pursuing Grant Funds in AcademiaShalin Hai-Jew
Starting as an organization’s new grant writer can be a challenge, especially in a case where there has been a time lapse since the last one left. People get out of the habit of pursuing grant funds. This slideshow addresses some of the reasons for such reluctance and proposes some ways to mitigate these.
Writing grants is one common way that those in institutions of higher education may acquire some funds—small and big, one-off and continuing—to conduct research, hire faculty and researchers and learners and others, update equipment, update or build up new buildings, and achieve other work. This slideshow explores some aspects of the work of grant writing in the present moment in higher education.
Contrasting My Beginner Folk Art vs. Machine Co-Created Folk Art with an Art-...Shalin Hai-Jew
The SARS-CoV-2 pandemic inspired several years of experimentation with common or folk art, involving mixed media, alcohol ink painting, and other explorations. Then, with the emergence of art-making generative AIs, there were further experiments, particularly with one that enables generation of visuals from scanned art and photos, text prompts, style overlays, and text-based visual modifiers. While both types of artmaking are emotionally satisfying and helpful for stress management, there are some contrasting differences. This exploratory slideshow explores some of these differences in order to partially shed light on the informal usage of an art-making generative AI (artificial intelligence).
Creating Seeding Visuals to Prompt Art-Making Generative AIsShalin Hai-Jew
Art-making generative AIs have come to the fore. A basic work pipeline typically involves starting with text prompts -> generated images. That image may be used to seed further iterations. Deep Dream Generator (DDG) enables the application of “modifiers” of various types (artist styles, visual adjectives, others) to be applied in addition to the text prompt.
Another approach involves beginning with a “seeding image,” a born-digital or digitized (born-analog) visual on which AI-generated art may be based for a multi-channel and multi-modal prompt. This slideshow provides some observations of how to think about seeding images, particularly in terms of how the DDG handles them, with its “algorithmic pareidolia” (“Deep Dream,” Wikipedia, July 3, 2023).
Human art-making is often about throwing mass-scale conversations. Artists are thought to help bridge humanity into the future. Whether generative AI art enables this or not is still not clear.
Common Neophyte Academic Book Manuscript Reviewer MistakesShalin Hai-Jew
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Fashioning Text (and Image) Prompts for the CrAIyon Art-Making Generative AIShalin Hai-Jew
CrAIyon (formerly DALL-E after Salvador “Dali”) is a web-facing art-making generative AI tool online (https://www.craiyon.com/) that enables the uses of text (and image) prompts for the creation of watermarked, lightweight visuals. Counterintuitively, the rough visuals are much more usable for recombinations and remixes and recreations into usable digital visuals for various digital learning objects. The textual prompts are not particularly intuitive because of how the generative AI program was trained on mass-scale visuals). There is an art and occasional indirection to working prompts after each try, with the resulting nine-image proof sheets that CrAIyon outputs. The tool can be used iteratively for different outputs.
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Augmented Reality in Multi-Dimensionality: Design for Space, Motion, Multiple...Shalin Hai-Jew
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Some Ways to Conduct SoTL Research in Augmented Reality (AR) for Teaching and...Shalin Hai-Jew
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Exploring the Deep Dream Generator (an Art-Making Generative AI) Shalin Hai-Jew
The Deep Dream Generator was created by Google engineer Alexander Mordvintsev in 2014. It has a public facing instance at https://deepdreamgenerator.com/, which enables people to use text prompts and image prompts (individually or in combination) to inspire the art-generating generative AI to output images. This work highlights some process-based walk-throughs of the tool, some practical uses, some lightweight art learning, some aspects of the online social community on this platform, and other insights. Some works by the AI prompted by the presenter may be seen here: https://deepdreamgenerator.com/u/sjjalinn.
(This is the first draft of a slideshow that will be used in a conference later in the year.)
Augmented Reality for Learning and AccessibilityShalin Hai-Jew
Recently, the presenter conducted a systematic review of the academic literature and an environmental scan to learn how to set up an augmented reality (AR) shop at an institution of higher education. The ambition was to not only set up AR in an accessible and legal way but also be able to test for potential +/- effects of AR on teaching and learning. The research did not go past the review stage, because of a lack of funding, but some insights about accessibility in AR were acquired.
(The visuals are from Deep Dream Generator and CrAIyon.)
Engaging Pixabay as an open-source contributor to hone digital image editing,...Shalin Hai-Jew
This slideshow describes the author's early experiences with creating two accounts on Pixabay in order to advance digital editing skills in multimedia. The two accounts are located at https://pixabay.com/users/sjjalinn-28605710/ and https://pixabay.com/users/wavegenerics-29440244/ ...
This work explores four main spaces where researchers publish about educational technology: academic-commercial, open-access, open-source, and self-publishing.
Human-Machine Collaboration: Using art-making AI (CrAIyon) as cited work, o...Shalin Hai-Jew
It is early days for generative art AIs. What are some ways to use these to complement one's work while staying legal (legal-ish)?
Correction: .webp is a raster format
Getting Started with Augmented Reality (AR) in Online Teaching and Learning i...Shalin Hai-Jew
University creative shops are exploring whether they can get into the game of producing AR-enhanced experiences: campus tours, interactive gaming, virtual laboratories, exploratory art spaces, simulations, design labs, online / offline / blended teaching and learning modules, and other AR applications.
This work offers a basic environmental scan of the AR space for online teaching and learning, and it includes pedagogical design leads from the current research, technological knowhow, hands-on design / development / deployment of learning objects, and online teaching and learning methods.
Improving Workplace Safety Performance in Malaysian SMEs: The Role of Safety ...AJHSSR Journal
ABSTRACT: In the Malaysian context, small and medium enterprises (SMEs) experience a significant
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human factors, particularly unsafe behaviors. This study, conducted in Malaysia's northern region, specifically
targeted Safety and Health/Human Resource professionals within the manufacturing sector of SMEs. We
gathered a robust dataset comprising 107 responses through a meticulously designed self-administered
questionnaire. Employing advanced partial least squares-structural equation modeling (PLS-SEM) techniques
with SmartPLS 3.2.9, we rigorously analyzed the data to scrutinize the intricate relationship between safety
behavior and safety performance. The research findings unequivocally underscore the palpable and
consequential impact of safety behavior variables, namely safety compliance and safety participation, on
improving safety performance indicators such as accidents, injuries, and property damages. These results
strongly validate research hypotheses. Consequently, this study highlights the pivotal significance of cultivating
safety behavior among employees, particularly in resource-constrained SME settings, as an essential step toward
enhancing workplace safety performance.
KEYWORDS :Safety compliance, safety participation, safety performance, SME
Multilingual SEO Services | Multilingual Keyword Research | Filosemadisonsmith478075
Multilingual SEO services are essential for businesses aiming to expand their global presence. They involve optimizing a website for search engines in multiple languages, enhancing visibility, and reaching diverse audiences. Filose offers comprehensive multilingual SEO services designed to help businesses optimize their websites for search engines in various languages, enhancing their global reach and market presence. These services ensure that your content is not only translated but also culturally and contextually adapted to resonate with local audiences.
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Surat Digital Marketing School is created to offer a complete course that is specifically designed as per the current industry trends. Years of experience has helped us identify and understand the graduate-employee skills gap in the industry. At our school, we keep up with the pace of the industry and impart a holistic education that encompasses all the latest concepts of the Digital world so that our graduates can effortlessly integrate into the assigned roles.
This is the place where you become a Digital Marketing Expert.
Your Path to YouTube Stardom Starts HereSocioCosmos
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“To be integrated is to feel secure, to feel connected.” The views and experi...AJHSSR Journal
ABSTRACT: Although a significant amount of literature exists on Morocco's migration policies and their
successes and failures since their implementation in 2014, there is limited research on the integration of subSaharan African children into schools. This paperis part of a Ph.D. research project that aims to fill this gap. It
reports the main findings of a study conducted with migrant children enrolled in two public schools in Rabat,
Morocco, exploring how integration is defined by the children themselves and identifying the obstacles that they
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children‟s agency when being integrated into mainstream public schools.
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KEYWORDS: migration, education, integration, sub-Saharan African children, public school
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Explore the latest trends in Search Engine Optimization (SEO) and discover how modern practices are transforming business visibility. This document delves into the shift from keyword optimization to user intent, highlighting key trends such as voice search optimization, artificial intelligence, mobile-first indexing, and the importance of E-A-T principles. Enhance your online presence with expert insights from Digital Marketing Lab, your partner in maximizing SEO performance.
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Hashtag Conversations,Eventgraphs, and User Ego Neighborhoods: Extracting Social Network Data from Twitter
1. Hashtag Conversations,
Eventgraphs,
and User Ego Neighborhoods:
Extracting Social Network Data
from Twitter
Shalin Hai-Jew
Kansas State University
2014 National Extension Technology Conference
May 2014
2. Presentation Overview
• This introduces methods for extracting and analyzing social network
data from Twitter for hashtag conversations (and emergent events),
event graphs, search networks, and user ego neighborhoods (using
NodeXL). There will be direct demonstrations and discussions of how
to analyze social network graphs. This information may be extended
with human- and / or machine-based sentiment analysis.
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
2
3. Self-Intros
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
3
• Do you use Twitter? If so, how?
• Who do you follow on Twitter, and why?
• Have you analyzed your own social networks on Twitter? What’s the
company you keep (online)?
• Have you ever created a hashtag for a formal conference event?
Were you able to gain some insights about what your participants
were experiencing during the conference?
• What would you like to learn in this session?
* My goal for you is to
learn capability (what
is fairly easily
possible), not
method… Method is
for another day,
another time.
4. Twitter Social Networking and Microblogging
Social Media Platform
• 140-character text-based Tweets
• Images (Twitpics) and videos (Vine)
• Accounts as humans, ‘bots (collecting and re-tweeting information,
sensor networks), and cyborgs (humans and ‘bots co-Tweeting)
• Created in 2006 and based out of San Francisco, California
• 500 million registered users in 2012
• 340 million Tweets a day as the “SMS of the Internet”
• Has attracted a range of public, private, and governmental
organizations; groups (religious, political, advocacy, and others);
individuals
• Has an application programming interface (API) which enables some
limited access to their public data
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
4
5. Electronic Social Network Analysis
• Extraction of social network data from social media platforms
(through their APIs): social networking sites, email systems, wikis,
blogs, microblogging sites, web networks, and others
• Node-link, vertex-edge, entity-relationship
• A form of structure mining with implications for
• Organizational analysis
• Entity (node) analysis
• Social ties
• Understandings of social structure and power
• Diffusion of innovation, information, culture, attitudes, and other
transmissible resources
• Electronic event analysis
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
5
7. Some Basics of E-SNA (cont.)
• Core-periphery dynamic and influence (and power) / “primary” and
“secondary” membership in the network
• Knowledge and influence
• Collection of resources
• Clustering
• Motif censuses, network structures, network topologies, geodesic
distance, connectivity
• Bridging
• Network structure, network topology
• Thick ties / tight coupling in electronic social spaces
• Thin ties / loose coupling in electronic social spaces
• Homophily vs. heterophily
• The company you keep
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
7
8. Some Basics of E-SNA (cont.)
Global Social Network Structures
• Betweenness centrality
(shortest path betweenness
centrality)
• Closeness centrality (closeness
of a node to all other nodes in
the network graph)
• Eigenvector centrality
(closeness to important
neighbors)
• Clustering coefficient (the
amount of clustering in a
network)
Local Social Network Structures
• Degree centrality (in-degree and
out-degree)
• Clustering coefficient
(embeddedness)
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
8
9. Units of Analysis
• Entity: Node or vertex
• Relationships: Links, edges
• Dyads, triads, … motifs (different relational structures)
• Clusters and sub-clusters (groups or meta-nodes)
• Islands
• Pendants (one node, one link); whiskers (one link, multiple nodes)
• Isolates
• Ego neighborhoods
• Social network
• Multiple social networks
• “Big data” universes
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
9
10. Why Learn about Electronic Social Networks?
• Understand respective roles in the community
• Identify informally influential individuals who are otherwise hidden
• Monitor what messages are moving through the network to
understand public sentiment and understandings
• Plan diffusion of prosocial information and actions; head off negative
diffusions in a social network
• Wire new networks for social and individual resilience (such as
regarding health, emotion, economics, and other)
• Rewire social networks for different objectives and aims; optimize
social groups based on what is known about people’s socializing and
preferences
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
10
11. E-SNA on Twitter….
• Hashtag conversations (#)
• Event graphs (unfolding formal and informal events by hashtags and
key words)
• Search networks
• Understanding user (account) social networks
• Ego neighborhoods on Twitter (direct alters)
• Clusters and sub-clusters; islands; pendants; isolates
• Motif censuses
• Egos
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
11
12. Questions so Far?
• What do you think about (electronic) social network analysis (and
structure mining)? Do you think that the assumptions are valid?
Why or why not?
• What do you think about electronic social network analysis?
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
12
13. Hashtag Conversations
• Narrow-casting (to a distinct small group) and broad-casting
(communicating broadly to any who care to follow)
• Identifying the messages shared
• Sentiments
• Semantics
• Main conversationalists
• Calls to action
• Identifying the networks of accounts in connection to each other
around this discussion
• Observing the interactions between accounts (nodes or vertices)
around the particular discussion
• Identifying the “mayor of your hashtag” (using Dr. Marc A. Smith’s
phrasing) or the influential discussants and their important (central,
widely followed, re-tweeted) messaging
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
13
14. Eventgraphs
• Mapped networks of interactions based around a physical or virtual
or other event (in this case)
• Formal, informal, or semi-formal
• Planned or unplanned events
• Conferences with disambiguated or original hashtags; may include online or augmented
reality games to increase participation (planned)
• Accidents, mass health events, or unusual “spectacle” occurrences (unplanned)
• Micro (local or distributed) or mass (locationally clustered or distributed)
• Trending microblogging messaging over time (exponential messaging
to peaks or multiple peaks and gradual diminishment or steep drop-
off)
• Multimedial with microblogged text, images, and video; interactive;
dynamic
• Identification of the main geographical locations of the discussants
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
14
15. Search (Social) Networks (Online)
• Identification of
• particular topics in discussion (the less
ambiguity of the term, the better;
otherwise, the tools will track a broad
range of terms with various word senses)
• discussants (social media platform
accounts)
• main messaging of the discussants
(Tweet or microblogging streams)
• main physical locations of the discussants
(based on noisy geo information)
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
15
16. User Social Networks
• Node / vertex / entity / agent analysis
• Link / edge / arc / tie / relationship analysis
• Identification of the alters in the ego neighborhood
• Analysis of transitivity among the alters in the ego neighborhood
• Capture of a 2-degree social network on Twitter
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
16
17. Motif Censuses
• Understanding of the global nature of the network
• The power structures within the network
• The clusters, sub-clusters, islands, pendants, and isolates
• The social individuals and entities within the network
• The transmissibles moving through the network
• Static (vs. dynamic information captures)
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
17
18. The Data Extraction and Network
Visualization Tool: NodeXL
Network Overview, Discovery and Exploration for Excel
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
18
19. Network Overview, Discovery and Exploration
for Excel (NodeXL)
• NodeXL
• Free and open-source code
• Data scraping from social media
platforms through their respect APIs (of
publicly available information only)
• Add-on to Excel (formerly known as
NetMap)
• Available on the Microsoft CodePlex
platform
• Requires Windows (or parallels on Mac)
• Sponsored by the Social Media
Research Foundation
• NodeXL Graph Gallery for shared
graphs and datasets
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
19
20. Types of Data Extractions from Twitter
NodeXL (relations, structure, select
contents)
• #hashtag
• Search
• Twitter “List Network”
• Twitter User Network
NCapture of NVivo (semantics,
message contents)
• Twitter User Tweets
• Twitter List Tweets
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
20
21. Input Parameters
• Size of the crawl
• Degree of the crawl
• Image capture
• Tweet capture
• Direction (followed by/ following /
both)
• Edge definition: Followed /
following; replies-to; mentions
• Tweet column
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
21
22. Data Processing: Graph Metrics
• Degree, in-degree, out-degree
• Betweenness and closeness
centralities
• Eigenvector centrality
• Vertex clustering coefficient
• Vertex pagerank
• Edge reciprocation
• Words and word pairs
• Twitter search network top items
• …and others
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
22
23. Data Processing: Grouping
• Group by vertex attribute
• Group by connected component
• Group by cluster
• Group by motif
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
23
24. Data Visualization
• Type of layout algorithm applied to the data
• Autofill
• Labeling of vertices
• Labeling of edges
• Graph pane
• Graph options
• Zoom
• Scale
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
24
25. Dynamic Filtering
• Adjust parameters
(with the sliders) to
limit what is visualized
• Change up the time
zones to analyze what
is being
communicating and by
whom at which time
(UTC / coordinated
universal time)
• Capture broadly and
then focus in using
dynamic filtering
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
25
26. Data Analysis
• Use both the dataset and the visualizations (they both complement
each other and are necessary for full understanding)
• Capture the Tweets column and import that into a text analysis
software program
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
26
27. Limits -> Controlling for Input Parameters for
the Data Extraction
• Social media platform (Twitter
and its data processing rate
limits), even with an account for
“whitelisting” (and the time-of-
day of the data extraction
through its data-streaming API)
• NodeXL (up to about 300,000
records or so)
• Computational power of
researcher machine
• Computer memory of researcher
machine
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
27
• No early indicator of size of
data crawl or the acquire-
ability of the electronic social
network
• Costly (computational and
time expense) non-captures
at system limits
28. Addendum
• May apply Boolean operators into the query (and query multiple
terms simultaneously)
• May use macros
• May re-crawl using original parameters of a data extraction
• May automate data extractions
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
28
29. Some Sample
Graph Visualizations
From NodeXL Extractions from Twitter
29
Note: Other details have been excluded because these visualizations
are incomplete without the graph metrics and other complementary
data…and it would be misrepresentational to explain the contexts of
the data crawl behind the social network graphs incompletely. All of
these graphs may be found in fuller detail and some with downloadable
data sets on the NodeXL Graph Gallery. At the graph gallery, put “SHJ”
in the Search bar at the top right.
31. Circle Layout (Ring Lattice Graph)
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
31
32. Harel-Koren Fast Multiscale with Vertex
Labels
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
32
33. Random Layout Algorithm, Images at the
Vertices
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
33
34. Sugiyama Layout of Groups, Force-Based
Overall Network Layout
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
34
41. 3D Fruchterman-Reingold Force-Based Graph
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
41
42. Circle Layout / Ring Lattice Graph at Group
Level, Force-Based Layout at Network Level
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
42
45. Fruchterman-Reingold Layout, Imagery for
Vertices
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
45
46. Random Layout of Groups, Force-Based
Layout of Network with Combined Edges
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
46
47. Harel-Koren Fast Multiscale Layout at Cluster
Level, Force-Based Layout at Network Level
Hashtag Conversations, Eventgraphs, and User Ego
Neighborhoods: Extracting Social Network Data from Twitter
47
48. Motifs Extraction (Census), Sugiyama Layout
at Network Level
Hashtag Conversations, Eventgraphs, and User Ego
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49. Harel-Koren Fast Multiscale for Groups,
Force-Based Layout at Network Level
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50. Clustering by Clauset-Newman-Moore, Network
Layout with Harel-Koren Fast Multiscale
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51. Motifs at Group Level, Spiral at Network Level
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52. Random at Group Level, Packed Rectangles
for Network
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53. Harel-Koren Fast Multiscale for Clusters,
Treemap Layout for Network
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54. Horizontal Sine Wave Layout (on beta)
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61. Motif, Fruchterman-Reingold, on Grid
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62. Grid, Imagery on Vertices
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65. NodeXL Graph Server
• Continuous crawl based on a certain term or account for over a
month
• Academic purposes only
• Must be requested through Dr. Marc A. Smith (Connected Action Consulting
Group @ marc@connectedaction.net)
• Not retroactive crawls (a limitation of Twitter)
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67. Mixing Up Datasets
Twitter Data Grants
• Feb. 2014
• Twitter Engineering Blog
Other Sources
• Content-sharing sites (with
public APIs)
• YouTube
• Flickr
• Social networking sites (with
public APIs)
• Facebook
• LinkedIn
• Email Networks
• Web networks
• Wiki networks
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68. Semantic (Meaning) Analysis of a
Tweet Stream
Using NCapture (add-in to Google Chrome and MS Internet Explorer browsers) and
NVivo (a qualitative and mixed methods data analysis tool)
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69. (Partial) Twitter Feed Capture using NCapture
of NVivo 10
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70. Word Cloud based on Word Frequency Count
from Twitter Feed (Gist)
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71. Geolocation (Lat / Long) Data of Active Twitter
User Accounts on a Tweet Stream / Feed
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72. Word Similarity Analysis
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73. Word Frequency Treemap
(classical content analysis)
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74. Word Search Word Tree (and Stemming)
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75. Manual Analysis…through Coding,
Categorizing, and Evaluation
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• Data reduction
• Summary
• Matrix analysis
• Coding and analysis
Topic Pro (sentiment) Con (sentiment)
76. Human-Machine Analysis
• Network Text Analysis Theory (language modeled as networks of
words and relations)
• Semantic network
• Nodes: concepts or ideas, ideational kernels
• Links: statements, relationships (strength of relationship, directionality such
as agreement / disagreement or positive / negative, type of relation,
sentiment
• Network: semantic map, union of all statements
• May be a one-mode network (all nodes of a type)
• Concepts
• May be a multi-modal network (based on ontological coding with
various mixes of node types)
• Persons, places, concepts, sentiments, locations, and others
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77. Human-Machine Analysis (cont.)
• Meta-network analysis based on a text corpus / merged text
corpuses
• Drawn from unstructured natural language text data
• Identification of users (account holders on Twitter) and their
interrelationships with others based on messaging and re-Tweeting and
following / not following
• May use Carnegie Mellon University’s freeware text-mining tool
AutoMap 3.0.10.18 on Windows (by Center for Computational
Analysis of Social and Organizational Systems, CASOS) (2001 –
present)
• Graph visualizations in 2D and 3D made in ORA-NetScenes (CASOS)
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78. Human-Machine Analysis (cont.)
• AutoMap…requires data pre-processing (setting parameters)
• Requires text corpuses as .txt files (transcoding from .doc, .docx, .HTML, or
other)
• May combine multiple text sets (through merging); can then query on the
whole set or on the individual text sets
• May create “stop words” (or “delete”) lists to de-noise data (with “stop
words” like relative pronouns, personal pronouns, articles, conjunctions, and
other words with less semantic meaning, etc.)
• May use universal or domain-specific “thesauruses” to define, filter, and
hone the meta-network extractions
• Enables the defining of sentiment
• Requires testing of a sample set and meta network visualization to ensure
appropriateness of the data refinements
• Involves the design of meta-networks and ontologies from the text corpuses
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79. Human-Machine Analysis (cont.)
• …requires data processing and data visualization
• May run the textual data processing
• Includes a web scraper to main social media platforms in its ScriptRunner
feature
• …requires data post-processing
• Includes accessing AutoMap data from ORA-NetSense to create network
visualizations
• Includes data “mining” for meaning / sense-making (identification of
patterns)
• Includes data visualization analysis
• Note: The work may require re-running this cycle multiple times for
different data queries.
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80. Sampler: Wordle™ Word Cloud to Create an
Emergent Thesaurus
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81. Sampler: Excerpt from a Year’s Worth of a
Blog’s Text Corpus
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82. Sampler: @kstate_pres Tweets Visualization
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83. Demos?
• Would you like to see how to set up a simple data crawl from Twitter
using NodeXL? (Note: Twitter rate limiting may mean that a
completed data extraction may not be achieved, but you can at least
see what a basic setup may look like.)
• Any questions?
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84. Conclusion and Contact
• Dr. Shalin Hai-Jew
• Instructional Designer
• Information Technology Assistance Center
• Kansas State University
• 212 Hale Library
• 785-532-5262
• shalin@k-state.edu
• Thanks to Dr. Marc A. Smith, sociologist and Chief Social Scientist for
Connected Action, for generously presenting a webinar at K-State to
our faculty and staff. Also, Tony Capone, NodeXL developer, made
the NodeXL beta available to me and has been very gracious and
encouraging.
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